Query management for indexer clusters in hybrid cloud deployments

ABSTRACT

Embodiments of the present disclosure provide a method for performing search queries. The method comprises transmitting a list of active indexers in an indexer cluster from a cluster master for receipt by a first search head, wherein the cluster master is communicatively coupled with an indexer cluster comprising a plurality of indexers and the first search head. The method further comprises receiving a first slot request at the cluster master in response to a query from the first search head, wherein the first search head is operable to transmit the query to the active indexers for execution if granted the slot request. Further, the method comprises evaluating a plurality of policies to determine if the first slot request can be granted and responsive to a positive determination, transmitting an authorization token for a slot to the first search head.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit as a Continuation of application Ser.No. 15/582,424, filed Apr. 28, 2017, titled “SINGLE POINT OF DISPATCHFOR HYBRID CLOUD DEPLOYMENT FOR INDEXER CLUSTERS,” the entire contentsof the aforementioned are hereby incorporated by reference as if fullyset forth herein, under 35 U.S.C. § 120. The applicant(s) hereby rescindany disclaimer of claim scope in the parent application(s) or theprosecution history thereof and advise the USPTO that the claims in thisapplication may be broader than any claim in the parent application(s).

The present application is related to co-pending U.S. patent applicationSer. No. 15/582,372, filed Apr. 28, 2017, entitled “SINGLE POINT OFDISPATCH FOR MANGEMENT OF SEARCH HEADS IN A HYBRID CLOUD DEPLOYMENT OF AQUERY SYSTEM,” naming Ashish Mathew as inventor, and having attorneydocket number SPLK-0012-01.01US. That application is incorporated hereinby reference in its entirety and for all purposes.

BACKGROUND

The rapid increase in the production and collection of machine generateddata has created relatively large data sets that are difficult to query.The machine data can include sequences of time stamped records that mayoccur in one or more usually continuous streams. Further, machine dataoften represents some type of activity made up of discrete events.

Searching data requires different ways to express searches. Queryengines today allow users to search by the most frequently occurringterms or keywords within the data and generally have little notion ofevent based searching. Given the large volume and repetitivecharacteristics of machine data, users often need to start by narrowingthe set of potential search results using event-based search mechanismsand then, through examination of the results, choose one or morekeywords to add to their search parameters. Timeframes and event-basemetadata like frequency, distribution, and likelihood of occurrence areespecially important when searching data, but difficult to achieve withcurrent query engine approaches.

Also, users often generate arbitrary queries to produce statistics andmetrics about selected data fields that may be included in the data.Indexing may enable raw data records to be identified quickly, butoperations that examine/scan the individual data records may becomeprohibitively expensive as the size of the data set grows. Further, thearbitrary queries generated by the user can intentionally orinadvertently overload the query systems with high levels of concurrentsearches. Thus, systems that can query relatively large sets of data arethe subject of considerable innovation.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

A hybrid deployment can comprise both an on-premises environment and acloud-based environment. In a hybrid deployment, a client may configurean “on-premises” solution by installing a software application on servercomputers owned by the client and by configuring each server to operateas one or more of a forwarder, a cluster master, an indexer, a searchhead, etc. Further, in a cloud-based deployment, a service provider mayprovide “cloud-based” solution by managing computing resourcesconfigured to implement various aspects of the system, e.g., forwarders,indexers, search heads, cluster masters, etc. and by providing access tothe system to end users via a network.

In one or more embodiments, a hybrid deployment may cluster together agroup of indexers to be protected by a cluster master. Each search headcommunicates with a master node called a “cluster master” that providesthe search head with a list of indexers to which the search head candistribute portions of the query. The cluster master maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events.

In one or more embodiments, a search head may communicate with thecluster master before the search head distributes queries to indexers tosecure authorization to access the indexers and to discover theaddresses of active indexers. To prevent indexer clusters from potentialoverloading, the indexer clusters are protected by a stateless clustermaster, which includes several layers of quota checks and bandwidthchecks performed by the cluster master prior to allowing a search headto dispatch a query to the indexer cluster. Accordingly, the clustermasters may act as gatekeepers for all searches to be executed onindexer clusters.

In one or more embodiments, a query will only be dispatched to anindexer cluster (or indexer) if it satisfies the quota requirementsimposed by a cluster master for its associated indexer cluster. Forexample, a cluster master may impose a quota related to a maximum numberof concurrent searches that can be run on an associated indexer clusterat any given time. The cluster master, therefore, can be configured toact as a single point of dispatch for any load on a subset of indexerclusters. Search heads would consult the single point of dispatch priorto dispatching a query. In one or more embodiments, a query will beauthorized for dispatch by a cluster master if all the quotarequirements are met for the associated indexer cluster. In other words,prior to dispatching a query to the indexer clusters (or indexers), asearch head would need authorization from a cluster master associatedwith the indexer clusters. The authorization can be based on a number ofpolicies or factors, e.g., number of permitted concurrent searches onthe indexer clusters, type of searches, time range of searches, resourceusage metrics, etc.

According to one or more embodiments of the present disclosure, a methodis provided for performing search queries in a way that avoidsoverloading an indexer cluster or indexers with an unwanted orunauthorized high levels of concurrent searches. The method comprisestransmitting a slot request from a search head to a cluster master inresponse to a query, wherein the cluster master is communicativelycoupled with an indexer cluster comprising a plurality of indexers. Themethod further comprises receiving addresses of active indexers in theindexer cluster and a response to the slot request from the clustermaster. Responsive to a grant of a slot by the cluster master, themethod comprises using the addresses to transmit the query to the activeindexers. Additionally, the method comprises receiving results of thequery from the active indexers and releasing the slot to the clustermaster.

According to a second embodiment of the present disclosure anon-transitory computer-readable medium having computer-readable programcode embodied therein for causing a computer system to perform a methodfor performing a query is provided. The method comprises transmitting aslot request from a search head to a cluster master in response to aquery, wherein the cluster master is communicatively coupled with anindexer cluster comprising a plurality of indexers. The method furthercomprises receiving addresses of active indexers in the indexer clusterand a response to the slot request from the cluster master. Responsiveto a grant of a slot by the cluster master, the method comprises usingthe addresses to transmit the query to the active indexers.Additionally, the method comprises receiving results of the query fromthe active indexers and releasing the slot to the cluster master.

According to a third embodiment of the present disclosure a system isprovided for performing a query. The system comprises a processingdevice communicatively coupled with a memory and configured to: (a)transmit a slot request from a search head to a cluster master inresponse to a query, wherein the cluster master is communicativelycoupled with an indexer cluster comprising a plurality of indexers; (b)receive addresses of active indexers in the indexer cluster and aresponse to the slot request from the cluster master; (c) responsive toa grant of a slot by the cluster master, use the addresses to transmitthe query to the active indexers; (d) receive results of the query fromthe active indexers; and (e) release the slot to the cluster master.

According to a fourth embodiment of the present disclosure a method isprovided for performing a query. The method comprises transmitting alist of active indexers in an indexer cluster from a cluster master forreceipt by a first search head as part of a heartbeat response message,wherein the cluster master is communicatively coupled with an indexercluster comprising a plurality of indexers, and wherein the first searchhead is one of a plurality of search heads operable to becommunicatively coupled to the cluster master. The method furthercomprises receiving a first slot request at the cluster master inresponse to a query from the first search head, wherein the first searchhead is operable to transmit the query to the active indexers forexecution if granted the slot request. Further, the method comprisesevaluating a plurality of policies to determine if the first slotrequest can be granted and responsive to a determination that the firstslot request can be granted, transmitting an authorization token for aslot to the first search head.

According to a fifth embodiment of the present disclosure anon-transitory computer-readable medium having computer-readable programcode embodied therein for causing a computer system to perform a methodfor performing a query is provided. The method comprises transmitting alist of active indexers in an indexer cluster from a cluster master forreceipt by a first search head as part of a heartbeat response message,wherein the cluster master is communicatively coupled with an indexercluster comprising a plurality of indexers, and wherein the first searchhead is one of a plurality of search heads operable to becommunicatively coupled to the cluster master. The method furthercomprises receiving a first slot request at the cluster master inresponse to a query from the first search head, wherein the first searchhead is operable to transmit the query to the active indexers forexecution if granted the slot request. Further, the method comprisesevaluating a plurality of policies to determine if the first slotrequest can be granted and responsive to a determination that the firstslot request can be granted, transmitting an authorization token for aslot to the first search head.

According to a sixth embodiment of the present disclosure a system isprovided for performing a query. The system comprises a processingdevice communicatively coupled with a memory and configured to: (a)transmit a list of active indexers in an indexer cluster from a clustermaster for receipt by a first search head as part of a heartbeatresponse message, wherein the cluster master is communicatively coupledwith an indexer cluster comprising a plurality of indexers, and whereinthe first search head is one of a plurality of search heads operable tobe communicatively coupled to the cluster master; (b) receive a firstslot request at the cluster master in response to a query from the firstsearch head, wherein the first search head is operable to transmit thequery to the active indexers for execution if granted the slot request;(c) evaluate a plurality of policies to determine if the first slotrequest can be granted; and (d) responsive to a determination that thefirst slot request can be granted, transmit an authorization token for aslot to the first search head.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIG. 7 illustrates a user interface screen for an example datamodel-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example query received from a client and executedby search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 12 illustrates an exemplary manner in which time-stamped event datacan be stored in a data store in accordance with the disclosedembodiments;

FIG. 13 provides a visual representation of the manner in which apipelined query language or query operates in accordance with thedisclosed embodiments;

FIG. 14 illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed embodiments;

FIG. 15 illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments;

FIG. 16 shows a block diagram of an example of a hybrid deployment thatuses a cluster master to protect indexers from high levels of concurrentsearches in accordance with the disclosed embodiments;

FIG. 17 shows a block diagram illustrating the manner in which a clustermaster (or single point of dispatch) is used to authorize an incomingquery from a search head in accordance with the disclosed embodiments;

FIG. 18 shows a block diagram illustrating the manner in which heartbeatmessages between a cluster master and a query are used to communicateconfiguration policy information in accordance with the disclosedembodiments;

FIG. 19 presents a flowchart illustrating an exemplary process in whicha search head secures authorization from a cluster master prior todispatching a query in accordance with the disclosed embodiments.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

-   -   2.1. Host Devices    -   2.2. Client Devices    -   2.3. Client Device Applications    -   2.4. Data Server System    -   2.5. Data Ingestion        -   2.5.1. Input        -   2.5.2. Parsing        -   2.5.3. Indexing    -   2.6. Query Processing    -   2.7 Pipelined Query Language    -   2.8. Field Extraction Using A Configuration File    -   2.9. Example Search Screen    -   2.10. Data Modelling    -   2.11. Acceleration Techniques        -   2.11.1. Aggregation Technique        -   2.11.2. Keyword Index        -   2.11.3. High Performance Analytics Store            -   2.11.3.1 Extracting Event Data Using Posting Values        -   2.11.4. Accelerating Report Generation    -   2.12. Security Features    -   2.13. Data Center Monitoring    -   2.14. Cloud-Based System Overview        -   2.14.1 Performing Quota Checks Prior To Query Dispatch    -   2.15. Searching Externally Archived Data        -   2.15.1. ERP Process Features    -   2.16 IT Service Monitoring

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices, virtualmachines, operating systems, Internet of Things (IoT) devices, etc.Machine-generated data can include system logs, network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc. In general, machine-generated data can alsoinclude performance data, diagnostic information, and many other typesof data that can be analyzed to diagnose performance problems, monitoruser interactions, and to derive other insights.

A number of tools are available to analyze raw machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to query all of themachine data, instead of querying only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and querying massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. Further, new machine data is beingproduced in real-time, which requires that any searching or analysis ofthe data needs to be dynamic and updated continuously. By way ofexample, a server may be interacting with many different types ofcomponents in the IT environment in real-time, e.g., client device,operating system, routers, firewalls, etc. Each of the components may beproducing log files with information regarding its interaction with theserver in a different format. In order to determine if the server issecure, for example, data exchanged with all the various componentsneeds to be analyzed and correlated in real-time. In another example,mobile devices may generate large amounts of information relating todata accesses, application performance, operating system performance,network performance, etc. There can be millions of mobile devices thatreport these types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and querymachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a query pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by queryingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device or application-specific information. Monitoringcomponent 112 may be an integrated component of a client application110, a plug-in, an extension, or any other type of add-on component.Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent or received by a client application 110.For example, the monitoring component 112 may be configured to monitordata packets transmitted to or from one or more host applications 114.Incoming or outgoing data packets can be read or examined to identifynetwork data contained within the packets, for example, and otheraspects of data packets can be analyzed to determine a number of networkperformance statistics. Monitoring network traffic may enableinformation to be gathered particular to the network performanceassociated with a client application 110 or set of applications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 or client device 102. Forexample, a monitoring component 112 may be configured to collect deviceperformance information by monitoring one or more client deviceoperations, or by making calls to an operating system or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive raw machine data from avariety of input data sources 202, and one or more indexers 206 thatprocess and store the data in one or more data stores 208. Theseforwarders and indexers can comprise separate computer systems, or mayalternatively comprise separate processes executing on one or morecomputer systems. System 108 also includes a search head 210 that isresponsible for performing a query during a query phase. The search head210 allows users to query and visualize event data extracted from rawmachine data received from various data sources. In an embodiment, eachindexer returns partial responses for a subset of events to the searchhead 210 that combines the results to produce an answer for the query.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, or performing other datatransformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102 orhost devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a queryduring a query phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives raw machine data from an inputsource, such as a data source 202 shown in FIG. 2. A forwarder initiallymay receive the data as a raw data stream generated by the input source.For example, a forwarder may receive a data stream from a log filegenerated by an application server, from a stream of network data from anetwork device, or from any other source of data. In one embodiment, aforwarder receives the raw data and may segment the data stream into“blocks”, or “buckets,” possibly of a uniform data size, to facilitatesubsequent processing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file, the name of the file, stream, or aprotocol and port related to received network data. A source type fieldmay contain a value specifying a particular source type label for thedata. Additional metadata fields may also be included during the inputphase, such as a character encoding of the data, if known, and possiblyother values that provide information relevant to later processingsteps. In an embodiment, a forwarder forwards the annotated data blocksto another system component (typically an indexer) for furtherprocessing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andquerying. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. This process isreferred to as “event breaking” which, in one embodiment, may involvebreaking up the data blocks at regularly occurring time-stamps withinthe raw data using a pattern-matching algorithm.

In an embodiment, to organize the data into events, an indexer maydetermine a source type associated with each data block (e.g., byextracting a source type label from the metadata fields associated withthe data block, etc.) and refer to a source type configurationcorresponding to the identified source type. A source type can either bewell known, e.g., HTTP Web server logs, Windows event logs, etc. or canbe created by the user. The source type definition may include one ormore properties that indicate to the indexer to automatically determinethe boundaries of events within the data. In general, these propertiesmay include regular expression-based rules or delimiter rules where, forexample, event boundaries may be indicated by predefined characters orcharacter strings. These predefined characters may include punctuationmarks or other special characters including, for example, carriagereturns, tabs, spaces, line breaks, etc. If a source type for the datais unknown to the indexer, an indexer may infer a source type for thedata by examining the structure of the data. Then, it can apply aninferred source type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp. Note, however, that other default fields may beextracted during index time as well.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 12 illustrates an exemplary manner in which time-stamped event datacan be stored in a data store in accordance with the disclosedembodiments. As mentioned above, certain metadata fields, e.g., host1210, source 1211, source type 1212 and timestamps 1214 are generatedfor each event, and associated with a corresponding event when storingthe event data in a data store, e.g., data store 208. The metadata canbe extracted from the raw machine data, or supplied or defined by anentity, such as a user or computer system. The metadata fields canbecome part of the event data. Note that while the time-stamp metadatafield can be extracted from the raw data associated with each event, thevalues for the other metadata fields may be determined by the indexerbased on information it receives pertaining to the source of the data.

Even though certain default or user-defined metadata fields can beextracted from the raw data for indexing purposes, e.g., time-baseddata, all the raw machine data within each event can be maintained inits original condition. In other words, unless certain information needsto be removed for practical reasons (e.g. extraneous information,confidential information), all the raw machine data associated with anevent can be preserved and saved in the data store in field 1213 asshown in FIG. 12. Accordingly, the data store in which the event recordsare stored is sometimes referred to as a “raw record data store.” Theraw record data store contains a record of the raw event data taggedwith the various default fields.

Referring back to FIG. 12, events 1221, 1222 and 1223 are associatedwith a server access log that records all requests from multiple clientsthat have been processed by the server. Each of the events 1221-1223 isassociated with a discrete request made from a client device. The rawdata associated with an event extracted from a server access log willcomprise the IP address of the client 1203, the user id of the personrequesting the document 1204, the time the server finished processingthe request 1205, the request line from the client 1206, the status codereturned by the server to the client 1207, the size of the objectreturned to the client (in this case, the gif file requested by theclient) 1208 and the time spent to serve the request in microseconds1209. As seen in FIG. 12, all the raw data retrieved from the serveraccess log is retained and stored as part of each event in the datastore.

Event 1224 is associated with an entry in a server error log thatrecords any errors that the server encountered when processing a clientrequest. Similar to the events related to the server access log, all theraw data in the error log file pertaining to this event is preserved andstored as part of the event record 1224.

Saving minimally processed event data in a data store associated withmetadata fields in the manner shown in FIG. 12 is advantageous becauseit allows a search analyst to query all the machine data at search timeinstead of querying only a pre-specified set of data items. As mentionedabove, because the system maintains the underlying raw data and uses alate-binding schema for querying the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.In other words, the user is not compelled to know about all the fieldsof information that will be needed at data ingestion time. As a userlearns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules for use the next time the schema isused by the system.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword querying for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to query for events basedon a time range, thereby, making the query process time-sensitive andefficient. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event.Organizing events into buckets optimizes time-based querying because itallows an indexer to search only the relevant buckets when responding toa query. It also allows for events with recent timestamps, which mayhave a higher likelihood of being accessed, to be stored in a fastermemory to facilitate faster retrieval. In one or more embodiments,buckets can be designated as “hot,” “warm,” or “cold” depending on thetime range of the events contained within them. For example, a bucketmay be designated “hot” if it is still open and accepting new incomingevents. A bucket may be designated “warm” if it is not accepting anymore new data and its time range has been finalized. Further, a bucketmay be designated “cold” if the data it contains is historical and canbe archived in slower memory. The “hot” and “warm” buckets, for example,may be stored in faster flash memory, while the “cold” buckets may bestored on a hard disk.

Each indexer 206 may be responsible for storing and querying a subset ofthe events contained in a corresponding data store 208. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a query. Atblock 402, a search head receives a query from a client. At block 404,the search head analyzes the query to determine what portion(s) of thequery can be delegated to indexers and what portions of the query can beexecuted locally by the search head. At block 406, the search headdistributes the determined portions of the query to the appropriateindexers. In an embodiment, a search head cluster may take the place ofan independent search head where each search head in the search headcluster coordinates with peer search heads in the search head cluster toschedule jobs, replicate query results, update configurations, fulfillquery requests, etc. In an embodiment, the search head (or each searchhead) communicates with a master node (also known as a cluster master,not shown in Fig.) that provides the search head with a list of indexersto which the search head can distribute the determined portions of thequery. The master node maintains a list of active indexers and can alsodesignate which indexers may have responsibility for responding toqueries over certain sets of events. A search head may communicate withthe master node before the search head distributes queries to indexersto discover the addresses of active indexers.

At block 408, the indexers to which the query was distributed, querydata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer queries for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The querying operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “query jobs” and the client may retrievethe results by referring the query jobs.

The search head can also perform various operations to make the querymore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of query results. This speeds up queries that are performedon a periodic basis.

2.7. Pipelined Query Language

Splunk Processing Language (SPL), used in conjunction with the SPLUNK®ENTERPRISE system, can be utilized to make a query. SPL is a pipelinedquery language in which a set of inputs is operated on by a firstcommand in a command line, and then a subsequent command following thepipe symbol “|” operates on the results produced by the first command,and so on for additional commands. In other words, a query using SPLcomprises a series of consecutive commands that are delimited by pipe(|) characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for the next command (to the right of the pipe). Thisenables the user to refine or enhance the data at each step along thepipeline until the desired results are attained. A query can start withsearch terms at the beginning of the pipeline. These search terms arekeywords, phrases, Boolean expressions, key/value pairs, etc. thatspecify which events should be retrieved from the indexes. The retrievedevents can then be passed as inputs into a query command using a pipecharacter. The query commands comprise directives regarding what to dowith the events after they have been retrieved from the index(es). Forexample, query commands may be used to filter unwanted information,extract more information, evaluate new fields, calculate statistics,reorder the results, create an alert, create a chart or perform sometype of aggregate function, where an aggregate function can be used toreturn an aggregate value, e.g., an average value, a sum, a maximumvalue, a root mean square etc.

Accordingly, a pipeline query language is highly advantageous because itcan perform “filtering” as well as “processing” functions. In otherwords, a single query can include search term expressions as well asdata-analysis expressions. For example, search terms at the beginning ofa query can perform a “filtering” step by retrieving a set of eventsbased on a condition (e.g., records associated with server responsetimes of less than 1 microsecond). The results of the filtering step canthen be passed to a query command that performs a “processing” step(e.g. calculating an aggregate value related to the filtered events suchas the average response time of servers with response times of less than1 microsecond). Furthermore, the search term expressions allow events tobe filtered by keyword as well as field value criteria. For example, aquery can filter out all events containing the word “warning” or filterout all events where a field value associated with a field “clientip” is“10.0.1.2.”

The query results retrieved from the index in response to query terms atthe beginning of a query can be considered of as a dynamically createdtable. Each query command redefines the shape of that table. Eachindexed event can be considered a row with a column for each fieldvalue. Columns contain basic information about the data and also maycontain data that has been dynamically extracted at search time.

FIG. 13 provides a visual representation of the manner in which apipelined query language or query operates in accordance with thedisclosed embodiments. The query 1310 is inputted by the user into thesearch bar 602. The query comprises a search, the results of which arepiped to two commands (namely, command 1 and command 2) that follow thesearch step.

Disk 1302 represents the event data in the raw record data store, e.g.,similar to the event data illustrated in FIG. 12.

When a user query is processed, a search step will precede other querycommands in the pipeline in order to generate a set of events at block1320. For example, the query can comprise search terms“sourcetype=syslog ERROR” at the front of the pipeline as shown in FIG.13. Intermediate results table 1304 shows fewer rows because itrepresents the subset of events retrieved from the index that matchedthe search terms “sourcetype=syslog ERROR” from query command 1310. Byway of further example, instead of a search step, the set of events atthe head of the pipeline may be generated by a call to a pre-existinginverted index (as will be explained later).

At block 1322, the set of events generated in the first part of thequery may be piped to a query that searches the set of events forfield-value pairs or for keywords. For example, the second intermediateresults table 1306 shows fewer columns, representing the result of thetop command, “top user” which summarizes the events into a list of thetop 10 users and displays the user, count, and percentage.

Finally, at block 1324, the results of the prior stage can be pipelinedto another stage where further filtering or processing of the data canbe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 13, the “fields—percent” part ofcommand 1310 removes the column that shows the percentage, thereby,leaving a final results table 1308 without a percentage column. Indifferent embodiments, other query languages, such as the StructuredQuery Language (“SQL”), can be used to create a query.

2.8 Field Extraction Using a Configuration File

The search head 210 allows users to query and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to query and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query.

As mentioned above, the search system enables users to run queriesagainst the stored data to retrieve events that meet criteria specifiedin a query, such as containing certain keywords or having specificvalues in defined fields. FIG. 14 illustrates the manner in whichkeyword searches and field searches are processed in accordance withdisclosed embodiments.

If a user inputs a query into search bar 1401 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query engine of the SPLUNK® ENTERPRISE system searchesfor those keywords directly in the event data 1412 stored in the rawrecord data store. Note that while FIG. 14 only illustrates four events,the raw record data store (corresponding to data store 208 in FIG. 2)may contain records for millions of events.

As disclosed above, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, events 1403 to 1405 will be identified based onthe results returned from the keyword index. As also noted above, theindex contains reference pointers to the events containing the keyword,which allows for efficient retrieval of the relevant events from the rawrecord data store.

If a user searches for a keyword that has not been indexed by theindexer, the SPLUNK® ENTERPRISE system would nevertheless be able toretrieve the events by searching the event data for the keyword in theraw record data store directly as shown in FIG. 14. For example, if auser searches for the keyword “frank”, and the name “frank” has not beenindexed at index time, the SPLUNK® ENTERPRISE system will search theevent data directly and return the first event 1403. Note that whetherthe keyword has been indexed at index time or not, in both cases the rawdata with the events 1412 is accessed from the raw data record store toservice the keyword search. In the case where the keyword has beenindexed, the index will contain a reference pointer that will allow fora more efficient retrieval of the event data from the data store. If thekeyword has not been indexed, the query engine will need to searchthrough all the records in the data store to service the query.

In most cases, however, in addition to keywords, a user's query willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield can also be multivalued, that is, it can appear more than once inan event and have a different value for each appearance, e.g., emailaddress fields. Fields are searchable name and value pairings thatdistinguish one event from another. Some examples of fields are“clientip” for IP addresses accessing a web server, or the “From” and“To” fields in email addresses.

By way of further example, consider the query, “status=404”. This queryfinds events with “status” fields that have a value of “404.” When thequery is run, the query engine does not look for events with any other“status” value. It also does not look for events containing other fieldsthat share “404” as a value. As a result, the query returns a set ofresults that are more focused than if “404” had been used in the searchstring as part of a keyword search. Note also that fields can appear inevents as “key=value” pairs such as “user_name=Bob.” But in most cases,field values appear in fixed, delimited positions without identifyingkeys. For example, the data store may contain events where the“user_name” value always appears by itself after the timestamp asillustrated by the following string: “Nov 15 09:33:22 johnryan.”

The SPLUNK® ENTERPRISE system advantageously allows for search timefield extraction. Fields can be extracted from the event data at searchtime using late-binding schema as opposed to at data ingestion time,which was a major limitation of the prior art systems.

In response to receiving the query, search head 210 uses extractionrules to extract values for the fields associated with a field or fieldsin the event data being queried. The search head 210 obtains extractionrules that specify how to extract a value for certain fields from anevent. Extraction rules can comprise regex rules that specify how toextract values for the relevant fields. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, atransformation rule may truncate a character string, or convert thecharacter string into a different data format. In some cases, the queryitself can specify one or more extraction rules.

FIG. 14 illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a query, the SPLUNK®ENTERPRISE system determines if the query references a “field.” Forexample, a query may request a list of events where the “clientip” fieldequals “127.0.0.1.” If the query itself does not specify an extractionrule and if the field is not a metadata field, e.g., time, host, source,source type, etc., then in order to determine an extraction rule, thequery engine may, in one or more embodiments, need to locateconfiguration file 1402 during the execution of the query as shown inFIG. 14.

Configuration file 1402 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some embodiments, the extraction rules can compriseregular expression rules that are manually entered in by the user.Regular expressions match patterns of characters in text and are usedfor extracting custom fields in text.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression, whichextracts those fields from similar events, and store the regularexpression as an extraction rule for the associated field in theconfiguration file 1402.

In some embodiments, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 1402. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 can apply the extraction rules derived fromconfiguration file 1402 to event data that it receives from indexers206. Indexers 206 may apply the extraction rules from the configurationfile to events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more embodiments, the extraction rule in configuration file 1402will also need to define the type or set of events that the rule appliesto. Because the raw record data store will contain events from multipleheterogeneous sources, multiple events may contain the same fields indifferent locations because of discrepancies in the format of the datagenerated by the various sources. Furthermore, certain events may notcontain a particular field at all. For example, event 1409 also contains“clientip” field, however, the “clientip” field is in a different formatfrom events 1403-1405. To address the discrepancies in the format andcontent of the different types of events, the configuration file willalso need to specify the set of events that an extraction rule appliesto, e.g., extraction rule 1406 specifies a rule for filtering by thetype of event and contains a regular expression for parsing out thefield value. Accordingly, each extraction rule will pertain to only aparticular type of event. If a particular field, e.g., “clientip” occursin multiple events, each of those types of events would need its owncorresponding extraction rule in the configuration file 1402 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. The most common way to categorizeevents is by source type because events generated by a particular sourcecan have the same format.

The field extraction rules stored in configuration file 1402 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query engine would first locate theconfiguration file 1402 to retrieve extraction rule 1406 that wouldallow it to extract values associated with the “clientip” field from theevent data 1410 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the queryengine can then execute the field criteria by performing the compareoperation to filter out the events where the “clientip” field equals“127.0.0.1.” In the example shown in FIG. 14, events 1403-1405 would bereturned in response to the user query. In this manner, the query enginecan service queries containing field criteria in addition to queriescontaining keyword criteria (as explained above).

The configuration file can be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and querying a subset of the events contained ina corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers (asdescribed in U.S. application Ser. No. 14/815,973, filed Aug. 1, 2015,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS” and in U.S. application Ser. No. 15/007,185, filedJan. 26, 2016, entitled “GENERATING AND STORING SUMMARIZATION TABLES FORSEARCHABLE EVENTS”, both of which are hereby incorporated by referencein their entirety for all purposes).

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user can create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for querying the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw datalong after data ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions can be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the SPLUNK® ENTERPRISE system to query and correlatedata across heterogeneous sources flexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 1402 allows the record data store1402 to be field searchable. In other words, the raw record data store1402 can be queried using keywords as well as fields, wherein the fieldsare searchable name/value pairings that distinguish one event fromanother and can be defined in configuration file 1402 using extractionrules. In comparison to a search containing field names, a keywordsearch does not need the configuration file and can search the eventdata directly as shown in FIG. 14.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file can be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined query language. Using the prior example, a usercould pipeline the results of the compare step to an aggregate functionby asking the query engine to count the number of events where the“clientip” field equals “127.0.0.1.”

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to query the log data from the three systems thatone or more indexers 206 are responsible for querying, thereby obtainingcorrelated information, such as the order number and correspondingcustomer ID number of the person placing the order. The system alsoallows the administrator to see a visualization of related events via auser interface. The administrator can query the search head 210 forcustomer ID field value matches across the log data from the threesystems that are stored at the one or more indexers 206. The customer IDfield value exists in the data gathered from the three systems, but thecustomer ID field value may be located in different areas of the datagiven differences in the architecture of the systems—there is a semanticrelationship between the customer ID field values generated by the threesystems. The search head 210 requests event data from the one or moreindexers 206 to gather relevant event data from the three systems. Itthen applies extraction rules to the event data in order to extractfield values that it can correlate. The search head may apply adifferent extraction rule to each set of events from each system whenthe event data format differs among systems. In this example, the userinterface can display to the administrator the event data correspondingto the common customer ID field values 507, 508, and 509, therebyproviding the administrator with insight into a customer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.9. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a query string. It also includes atime range picker 612 that enables the user to specify a time range forthe query. The SPLUNK® ENTERPRISE system is adept at handling bothreal-time queries and historical queries. For “historical queries” theuser can select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday” or “last week.” For “real-timequeries,” the user can select the size of a preceding time window toquery for real-time events. A “real-time” query can be open-ended, e.g.,a query could request any events where the server response time is over1 second in the last hour and further request that the query resultscontinue to be updated. Search screen 600 also initially displays a“data summary” dialog as is illustrated in FIG. 6B that enables the userto select different sources for the event data, such as by selectingspecific hosts and log files.

After the query is executed, the search screen 600 in FIG. 6A candisplay the results through query results tabs 604, wherein queryresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the query; a “statistics tab” thatdisplays statistics about the query results; and a “visualization tab”that displays various visualizations of the query results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.10. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized queries of thosedatasets. Those queries, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly query data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a query for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly query source data for eventsrelating to sent e-mails, and may be provided with a listing of the setof fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a query (typically anarrower query) that produces a subset of the events that would beproduced by the parent data model's query. The child's set of fields caninclude a subset of the set of fields of the parent data model oradditional fields. Data model objects that reference the subsets can bearranged in a hierarchical manner, so that child subsets of events areproper subsets of their parents. A user iteratively applies a modeldevelopment tool (not shown in Fig.) to prepare a query that defines asubset of events and assigns an object name to that subset. A childsubset is created by further limiting a query that generated a parentsubset. A late-binding schema of field extraction rules is associatedwith each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 July 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the query feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine or filter query results to produce more precise reports. The usermay select some fields for organizing the report and select other fieldsfor providing detail according to the report organization. For example,“region” and “salesperson” are fields used for organizing the report andsales data can be summarized (subtotaled and totaled) within thisorganization. The report generator allows the user to specify one ormore fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate query results across sets of events and generatestatistics based on aggregated query results. Building reports using thereport generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform queries of other data.

FIG. 7 illustrates a user interface screen where a user may selectreport generation options using data models. The report generationprocess may be driven by a predefined data model object, such as a datamodel object defined or saved via a reporting application or a datamodel object obtained from another source. A user can load a saved datamodel object using a report editor. For example, the initial query andfields used to drive the report editor may be obtained from a data modelobject. The data model object that is used to drive a report generationprocess may define a query and a set of fields. Upon loading of the datamodel object, the report generation process may enable a user to use thefields (e.g., the fields defined by the data model object) to definecriteria for a report (e.g., filters, split rows/columns, aggregates,etc.) and the query may be used to identify events (e.g., to identifyevents responsive to the query) used to generate the report. That is,for example, if a data model object is selected to drive a reporteditor, the graphical user interface of the report editor may enable auser to define reporting criteria for the report using the fieldsassociated with the selected data model object, and the events used togenerate the report may be constrained to the events that match, orotherwise satisfy, the query constraints of the selected data modelobject.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7 may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial query may be selected. That is, forexample, all of the fields of the identified data model object fieldsmay be selected. If the user selects the “Selected Fields” option 703,only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial query that a given field appears in. Thus, for example, if anobject dataset includes 10,000 events returned in response to an initialquery, and the “avg_age” field appears in 854 of those 10,000 events,then the “avg_age” field would have a coverage of 8.54% for that objectdataset. If, for example, the user selects the “Coverage” option andspecifies a coverage value of 2%, only fields having a coverage valueequal to or greater than 2% may be selected. The number of fieldscorresponding to each selectable option may be displayed in associationwith each option. For example, “97” displayed next to the “All Fields”option 702 indicates that 97 fields will be selected if the “All Fields”option is selected. The “3” displayed next to the “Selected Fields”option 703 indicates that 3 of the 97 fields will be selected if the“Selected Fields” option is selected. The “49” displayed next to the“Coverage” option 704 indicates that 49 of the 97 fields (e.g., the 49fields having a coverage of 2% or greater) will be selected if the“Coverage” option is selected. The number of fields corresponding to the“Coverage” option may be dynamically updated based on the specifiedpercent of coverage.

2.11. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing query operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below. Note that none of theacceleration techniques are mutually exclusive. They can all be usedtogether in the same system contemporanously.

2.11.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof query results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a query 802 receivedfrom a client at a search head 210 can split into two phases, including:(1) subtasks 804 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a queryresults aggregation operation 806 to be executed by the search head whenthe results are ultimately collected from the indexers.

During operation, upon receiving query 802, a search head 210 determinesthat a portion of the operations involved with the query may beperformed locally by the search head. The search head also determinedthe portion of the operations that may be distributed to the indexers.Typically, most of the computationally intensive operations will bedistributed to the indexers. The search head modifies query 802 bysubstituting “stats” (create aggregate statistics over results setsreceived from the indexers at the search head) with “prestats” (createstatistics by the indexer from local results set) to produce query 804,and then distributes query 804 to distributed indexers, which are alsoreferred to as “search peers.” The multiple indexers operate in paralleland each indexer operates on only a non-overlapping portion of theoverall data. Note that queries may generally specify query criteria oroperations to be performed on events that meet the query criteria.Queries may also specify field names, as well as search criteria for thevalues in the fields or operations to be performed on the values in thefields. Moreover, the search head may distribute the full query to thesearch peers as illustrated in FIG. 4, or may alternatively distribute amodified version (e.g., a more restricted version) of the query to thesearch peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 806 to form a single query result set.In this way, using map-reduce techniques allows each indexer to returnpartial responses for a subset of events to a search head that combinesthe results to produce an answer for the query. By executing the queryin this manner, the system effectively distributes the computationaloperations across the indexers while minimizing data transfers.

2.11.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indexes to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex (also known an “tokenizing”), an indexer first identifies a set ofkeywords. Then, the indexer includes the identified keywords in anindex, which associates each stored keyword with references to eventscontaining that keyword, or to locations within events where thatkeyword is located. When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword.

2.11.3. High Performance Analytics Store

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some embodiments of system108 create a high performance analytics store, which is referred to as a“summarization table,” (also referred to as a “lexicon” or “invertedindex”) that contains entries for specific field-value pairs. Each ofthese entries keeps track of instances of a specific value in a specificfield in the event data and includes references to events containing thespecific value in the specific field. For example, an example entry inan inverted index can keep track of occurrences of the value “94107” ina “ZIP code” field of a set of events and the entry includes referencesto all of the events that contain the value “94107” in the ZIP codefield. Creating the inverted index data structure avoids needing toincur the computational overhead each time a statistical query needs tobe run on a frequently encountered field-value pair. In order toexpedite queries, in most embodiments, the query engine will employ theinverted index separate from the raw record data store to generateresponses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-know compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someembodiments, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searachable data store.In some embodiments, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more embodiments, an inverted index is generated in responseto a user-initiated collection query. The term “collection query” asused herein refers to queries that include commands that generatesummarization information and inverted indexes (or summarization tables)from event records stored in the field searchable data store.

Note that a collection query is a special type of query that can beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more embodiment, a query cancomprise an initial step that calls up a pre-generated inverted index onwhich further filtering and processing can be performed. For example,referring back to FIG. 13, a set of events can be generated at block1320 by either using a “collection” query to create a new inverted indexor by calling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery because creating a new inverted index using a “collection query”can have a high computational overhead.

FIG. 15 illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments. As shown in FIG. 15,an inverted index 1502 can be created in response to a user-initiatedcollection query using the event data 1503 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 1502 being generated from the event data 1503 as shown in FIG. 15.Each entry in invertex index 1502 inclues an event reference value thatreferences the location of a source record in the field searchable datastore. As mentioned above, the reference value may be used to access theoriginal event record directly from the field searchable data store.

Note that while inverted index 1502 is represented in FIG. 15 ascomprising a separate entry for each occurrence of a field value pair inthe event records, the inverted index data structure is not limited tobeing represented as such in memory. For example, an inverted index maycontain only one entry for each unique field value pair and all thereference values for that particular field value pair may be stored aspart of a single entry (e.g. each entry may comprise multiple columns,wherein each column stores a separate reference value corresponding toeach occurrence of the particular field value pair within the eventrecords). In other embodiments, the information contained in an invertedindex may be represented within a data structure in multiple differentways within memory. Inverted index 1502 provides just one example of adata structure for an inverted index.

In one or more embodiments, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the various embodiments,one or more of the fields used in the summarization information may belisted in the collection query and/or they may be determined based onterms included in the collection query. For example, a collection querymay include an explicit list of fields to summarize. Or, in at least oneof the various embodiments, a collection query may include terms orexpressions that explicitly define the fields, e.g., using regex rules.Referring back to the example in FIG. 15, prior to running thecollection query that generates the inverted index 1502, the field name“clientip” may need to be defined in a configuration file by specifyingthe “access_combined” source type and a regular expression rule to parseout the client IP address. Alternatively, the collection query maycontain an explicit definition for the field name “clientip” which mayobviate the need to reference the configuration file at search time.

In one or more embodiments, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 1502 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 1502 with event data for any new events with the “clientip” vauleof “127.0.0.1.”

In some embodiments, the inverted indexes that include fields, values,and reference value (e.g., inverted index 1502) for event records may beincluded in the summarization information provided to the user. In otherembodiments, a user may not be interested in specific fields and valuescontained in the inverted index, but may need to perform a statisticalquery on the data in the inverted index. For example, referencing theexample of FIG. 15, rather than viewing the fields within summarizationtable 1502, a user may want to generate a count of all client requestsfrom IP address “127.0.0.1.” In this case, the query engine would simplyreturn a result of “4” rather than including details about the invertexindex 1502 in the information provided to the user.

The pipelined query language, e.g., SPL of the SPLUNK® ENTERPRISE systemcan be used to pipe the contents of an inverted index to a statisticalquery using the “stats” command for example. A “stats” query refers toqueries that generate result sets that may produce aggregate andstatistical results from event records, e.g., average, mean, max, min,rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 1502 can be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or searching the event records.Acccordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system can examine theentry in the inverted index to count instances of the specific value inthe field without having to go through the individual events or performdata extractions at search time.

In some embodiments, the system maintains a separate inverted index foreach of the above-described time-specific buckets that stores events fora specific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. Alternatively, the system can maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more embodiments, if one or more ofthe queries is a stats query, each indexer may generate a partial resultset from previously generated summarization information. The partialresult sets may be returned to the search head that received the queryand combined into a single result set for the query

As mentioned above, the inverted index can be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query can beinitiated by a user, or can be scheduled to occur automatically atspecific time intervals. A periodic query can also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some embodiments, if summarization information is absentfrom an indexer that includes responsive event records, further actionsmay be taken, such as, the summarization information may generated onthe fly, warnings may be provided the user, the collection queryoperation may be halted, the absence of summarization information may beignored, or the like, or combination thereof.

In one or more embodiments, an inverted index may be set up to updatecontinually. For example, the query may ask for the inverted index toupdate its result periodically, e.g., every hour. In such instances, theinverted index may be a dynamic data structure that is regularly updatedto include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system can use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results can then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the query to get the mostrecent results can be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety.

2.11.3.1 Using Reference Values in an Inverted Index to Retrieve andExtract Further Information From Associated Event Data

In one or more embodiments, if the system needs to process all eventsthat have a specific field-value combination, the system can use thereferences in the inverted index entry to directly access the events toextract further information without having to search all of the eventsto find the specific field-value combination at search time. In otherwords, the system can use the reference values to locate the associatedevent data in the field searchable data store and extract furtherinformation from those events, e.g., extract further field values fromthe events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuescan be directed for further filtering or processing in a query using thepipeline query language. The pipelined query language will, in oneembodiment, include syntax that can direct the initial filtering step ina query to an inverted index. In one embodiment, a user would includesyntax in the query that explicitly directs the initial searching orfiltering step to the inverted index.

Referencing the example in FIG. 15, if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new query or re-generating the inverted index with anadditional field, the user can generate a query that explicitly directsor pipes the contents of the already generated inverted index 1502 toanother filtering step requesting the user ids for the entries ininverted index 1502 where the server response time is greater than“0.0900” microseconds. The query engine would use the reference valuesstored in inverted index 1502 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “carlos” would be returned to the user from the generatedresults table 1504.

In one embodiment, the same methodology can be used to pipe the contentsof the inverted index to a processing step. In other words, the user isable to use the inverted index to efficiently and quickly performaggregate functions on field values that were not part of the initiallygenerated inverted index. For example, a user may want to determine anaverage object size (size of the requested gif) requested by clientsfrom IP address “127.0.0.1.” In this case, the query engine would againuse the reference values stored in inverted index 1502 to retrieve theevent data from the field searchable data store and, further, extractthe object size field values from the associated events 1511, 1512, 1513and 1514. Once, the corresponding object sizes have been extracted (i.e.2326, 2900, 2920, and 5000), the average can be computed and returned tothe user.

In one embodiment, instead of explicitly invoking the inverted index ina user-generated query, e.g., by the use of special commands or syntax,the SPLUNK® ENTERPRISE system can be configured to automaticallydetermine if any prior-generated inverted index can be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index1502. The query engine, in this case, would automatically determine thatan inverted index 1502 already exists in the system that could expeditethis query. In one embodiment, prior to running any query comprising afield-value pair, for example, a query engine may search though all theexisting inverted indexes to determine if a pre-generated inverted indexcould be used to expedite the query comprising the field-value pair.Accordingly, the query engine would automatically use the pre-generatedinverted index, e.g., index 1502 to generate the results without anyuser-involvement that directs the use of the index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new query.

As explained above, the SPLUNK® ENTERPRISE system includes one or moreforwarders that receive raw machine data from a variety of input datasources, and one or more indexers that process and store the data in oneor more data stores. By distributing events among the indexers and datastores, the indexers can analyze events for a query in parallel. In oneor more embodiments, a multiple indexer implementation of the querysystem would maintain a separate and respective inverted index for eachof the above-described time-specific buckets that stores events for aspecific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. As explained above, a search head would be able tocorrelate and synthesize data from across the various buckets andindexers.

This feature advantageously expedites queries because instead ofperforming a computationally intensive query in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly query an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the query to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one embodiment, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the query engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system can usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

2.11.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageosly onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.12. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved queries, and correlationqueriesto provide visibility into security-relevant threats and activityand generate notable events for tracking. The App enables the securitypractitioner to investigate and explore the data to find new or unknownthreats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation queries” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation query provided by theapplication. Note that correlation queries can be run continuously or atregular intervals (e.g., every hour) to search for notable events. Upondetection, notable events can be stored in a dedicated “notable eventsindex,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.13. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE ® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.14. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14.1 Performing Quota Checks Prior to Query Dispatch

FIG. 16 shows a block diagram of an example of a hybrid deployment thatuses a cluster master that can help protect indexers from high levels ofconcurrent queries in accordance with the disclosed embodiments. Ahybrid system such as the one shown in FIG. 16, as indicated above, cancomprise both an on-premises environment such as SPLUNK® ENTERPRISE anda cloud-based environment such as SPLUNK CLOUD™.

As discussed above, a user of a data intake and query system 108 (asshown in FIG. 2) may install or configure, on computing devices owned oroperated by the user, one or more software applications that implement,for example, forwarders, a cluster master, search heads and indexers.For example, a user may install a software application on servercomputers owned by the user and configure each server to operate as oneor more of a forwarder, a cluster master, an indexer, a search head,etc. This arrangement generally may be referred to as an “on-premises”solution. As shown in FIG. 16, search heads 1605 a and 1605 b, indexercluster 1611 a and cluster master 1615 a comprise an example of anon-premises solution 1650. Note that search heads 1605 a and 1605 b mayalso represent search head clusters (as explained further below), whileindexer cluster 1611 a may also represent an individual indexer.

An indexer cluster, e.g., indexer cluster 1611 a is a cluster ofindexers that are associated with one another or with a common clustermaster, such as cluster master 1615 a. As discussed above, each searchhead communicates with a master node called a “cluster master” thatprovides the search head with a list of indexers to which the searchhead can distribute the portions of a query. The cluster master, e.g.,cluster master 1615 a can maintain a list of active indexers and canalso designate which indexers may have responsibility for responding toqueries over certain sets of events. In an embodiment, a search head maycommunicate with the cluster master before the search head distributesqueries to indexers to secure authorization to access the indexers inthe indexer cluster or discover the addresses of active indexers.

As illustrated in FIG. 16, in addition to an on-premises environment,the hybrid deployment also comprises a cloud environment 1660. Asmentioned earlier in reference to FIG. 10, one or more of the componentsof a data intake and query system instead may be provided as acloud-based service. In a cloud-based deployment, such as the deployment1660 shown in FIG. 16, a service provider may provide a cloud-based dataintake and query system by managing computing resources configured toimplement various aspects of the system (e.g., forwarders, indexers,search heads, etc.) and by providing access to the system to end usersvia a network.

As shown in FIG. 16, the cloud environment 1660 of the hybrid deploymentcomprises search heads 1604 a-c, cluster masters 1614 a and 1614 b, andindexer clusters 1610 a and 1610 b. Note, that both the cloud andon-premises deployment can be scaled to any number of search heads,cluster masters, indexer clusters, etc. Further, note that search heads1604 a-c may also represent search head clusters, while indexer clusters1610 a-b may also represent individual indexers. The components in thecloud environment may be directly managed by the service provider aspart of the cloud service. In other words, the service provider of adata intake and query system 1008 (as shown in FIG. 10) may install andconfigure, on computing devices, e.g. servers owned and operated by theservice provider, one or more software applications that implement, forexample, the search heads, cluster master and indexer clusters shown inFIG. 16. A subscriber may use a web browser or other application on aclient device, e.g. client device 1002 to access a web portal or otherinterface that enables the subscriber to, for example, configure thecomponents in the cloud environment or to execute a query on one or moresearch heads.

Note that in a typical hybrid configuration, the on-premises solutionmay only comprise one or more search heads, and the indexer cluster andcluster master, e.g., indexer cluster 1611 a and cluster master 1615 amay reside on the cloud. In some instances, however, if users prefer agreater level of control over the configuration of certain aspects ofthe system for privacy or security reasons, then the on-premisessolution may also comprise indexer cluster and cluster masters as shownin FIG. 16. In most cases, however, users may instead prefer anarrangement in which the user is not directly responsible for providingand managing the indexer clusters and cluster masters.

As discussed in reference to FIGS. 2 and 4, the search head isresponsible for performing a query during a search phase. The searchhead, e.g., search head 210 allows users to query and visualize eventdata extracted from raw machine data received from various data sources.In an embodiment, the search head analyzes the query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. The search headdistributes the determined portions of the query to the appropriateindexers. Each indexer can return partial responses for a subset ofevents to the search head that combines the results to produce an answerfor the query.

In an embodiment, a search head cluster may take the place of anindependent search head where each search head in the search headcluster coordinates with peer search heads in the search head cluster toschedule jobs, replicate query results, update configurations, fulfillquery requests, etc. The search head 1604 a-c and 1605 a-b shown in FIG.16 can, therefore, represent search head clusters in addition toindependent search heads. For example, search head 1605 a can representa sales department search head cluster while search head 1605 b may be asecurity department or IT department search head.

Similarly, indexer clusters can also be assigned to different groupswithin an organization. For example, indexer cluster 1610 a in the cloudcan be designated to the sales department for an organization whileindexer cluster 1610 b can be designated to an IT department for thesame organization. In an embodiment, users within an organization mayuse both indexer clusters in the cloud as well as indexer clusters onpremises. For example, a user may use the on-premises indexer cluster1611 a for the sales department if the information is of a confidentialnature. Meanwhile, the another user in the same organization may useindexer clusters 1610 a and 1610 b in the cloud for other departmentsthat do not require high levels of privacy and confidentiality.

Note, that an indexer cluster can also be a common resource that isshared by multiple departments within a single organization or bymultiple organizations. A system that allows a single indexer cluster tobe shared between multiple organizations can be referred to as a“multi-tenant” deployment. For example, several different organizationsmay need access to a single indexer cluster.

In hybrid deployments, the search heads and clustered indexers may beunder the control of different entities. For example, as shown in FIG.16, search heads 1605 a and 1605 b can be part of an on-premisesdeployment but a user can use search heads 1605 a and 1605 b to delegatequeries to indexer clusters 1610 a and 1610 b that are part of the clouddeployment. The user may be able to log on to the search heads using aclient device to submit the query or the search heads may be configuredto allow the user to submit and execute the query directly on the searchhead.

In various deployments, search heads and search head clusters areprotected from high levels of concurrent queries by several layers ofquota checks that are performed at the search head prior to querydispatch. For example, a search head may have a limitation on themaximum number of queries that can be run on it or the maximum number ofusers that can access it at any given time. This, however, does notguarantee protection for the indexer clusters or individual indexers ifthe environment has multiple search heads that use indexer clusters (orindexers) as a common resource. For example, if a single organizationhas multiple search heads, each assigned to a different departmentwithin the organization, but all the search heads accessed a singleindexer cluster (or indexer) to execute queries, quota limitations onthe search heads would not protect the indexer cluster. Similarly, in amulti-tenant deployment, if an indexer is being shared by search heads(or search head clusters) in multiple disparate entities ororganizations, quota limitations at the individual search head (orsearch head cluster) would not protect the shared indexer cluster.

One of the issues that typically arises with hybrid deployments is thatone or more search heads issuing queries to an indexer cluster (orindexer) can intentionally or unintentionally overload one or moreindexer clusters (or indexers), either on-premises or in the cloud, witha high volume of concurrent searches that may be unwanted orunauthorized. Such overloading may have detrimental or catastrophiceffects on the performance of the deployment.

For example, in a hybrid deployment, a user may inadvertentlymisconfigure a search head (that may be located either on-premises or inthe cloud) to perform a high number of concurrent queries, thereby,destabilizing the underlying indexer cluster (or indexers) executing thequeries, which in most instances would be located in the cloud.Alternatively, a user may also maliciously run a high volume ofconcurrent searches to intentionally sabotage an indexer cluster thatmay be in the cloud.

This presents a serious problem for the indexer cluster because it maycause queries for other users (in either the same organization ordifferent organizations) to suspend and indexers to stop communicating.In other words, from the perspective of the other users of the system,queries in progress may be suspended or delayed without providing userswith any indication as to the cause of the performance degradation.Therefore, a single inadvertent or intentional query can affect allqueries that use the same underlying indexer cluster (or indexers) inthe cloud and render the entire deployment unusable for all users.

In hybrid deployments, therefore, especially where the search head isnot controlled by the entity as the indexer cluster, the search head (orsearch head cluster) cannot be trusted to respect query concurrencylimits on the indexer clusters (or indexers). Accordingly, a need existsto provide service providers with more control over the query load thatis allowed on the indexer clusters (or indexers).

In various embodiments, a search head may communicate with the clustermaster before the search head distributes queries to indexers to secureauthorization to access the indexers and to discover the addresses ofactive indexers. In other words, to prevent indexer clusters frompotential overloading, the indexer clusters are protected by a statelesscluster master, which includes several layers of quota and bandwidthchecks performed by the cluster master prior to allowing a search headto dispatch a query to the indexer cluster. The cluster master isstateless because a service provider may install and configure one ormore software applications on any computing devices, e.g., servers ownedand operated by the service provider that implement the cluster master.In other words, a computing device, e.g., a server acting as a clustermaster can easily be swapped out and replaced by another device runningsoftware that allows it to act as the cluster master.

For example, cluster masters 1614 a and 1614 b may act as gatekeepersfor all queries to be executed on indexer clusters 1610 a and 1610 brespectively. A query will only be dispatched to an indexer cluster (orindexer) if it satisfies the local quota requirements imposed at theindividual search head (or search head cluster) and if it satisfies theglobal quota requirements imposed by a cluster master for its associatedindexer cluster. For example, a cluster master may impose a quotarelated to a maximum number of concurrent queries that can be run on anassociated indexer cluster at any given time.

The cluster master, therefore, can be configured to act as a singlepoint of dispatch for any load on a subset of indexer clusters. Searchheads would consult the single point of dispatch prior to dispatching aquery. A query will be authorized for dispatch by a cluster master ifall the global quota requirements are met for the associated indexercluster. In other words, prior to dispatching a query to the indexerclusters (or indexers), a search head would need authorization from acluster master associated with the indexer clusters. The authorizationcan be based on a number of factors, e.g., number of permittedconcurrent queries on the indexer clusters, type of queries, time rangeof queries, resource usage metrics, etc.

As noted previously, the cluster master provides the search head with alist of active indexers in the indexer cluster (including theiraddresses) to which the search head can distribute the determinedportions of the query. The cluster master, e.g., cluster master 1615amaintains a list of active indexers and can also designate whichindexers may have responsibility for responding to queries over certainsets of events. In an embodiment, cluster masters communicate withsearch heads using heartbeat messages, which are periodic messages thatare sent from the search head to the cluster master requesting anupdated list of indexers available for executing queries.

In response, a cluster master provides a heartbeat response message backto the search head with an updated list of available indexers includingtheir addresses. The cluster master receives periodic heartbeat messagesfrom the associated indexer cluster itself and, therefore, always hasupdated information regarding available indexers.

In an embodiment, a cluster master uses the response heartbeat messageto provide additional information to the search heads regarding policiespertaining to authorizing a query. In other words, as part of theresponse heartbeat message, the cluster master can periodically respondback with the policies, e.g., related to the global quota requirements,based on which it will authorize a query on the associated indexercluster. In other words, the cluster master can publish policies at thesearch head related to running queries. For example, a cluster mastercan use a heartbeat response message to communicate the manner in whichthe search head should deal with a query when no more slots areavailable. It may, for example, inform the search head that it shouldcancel any requests for which it can find no available slot. Or it mayinform the search head that it should queue any queries for which noslots are available.

FIG. 17 shows a block diagram illustrating the manner in which a clustermaster (or single point of dispatch) is used to authorize an incomingquery from a search head in accordance with the disclosed embodiments.When attempting to run a query, a search head 1702 first requests a slotfrom the cluster master 1706. This request is typically an out of bandrequest and is not part of the synchronous heartbeat messages exchangedbetween a cluster master and a search head.

In response to receiving the request, the cluster master will evaluatethe query and check against all the applicable policies set by a serviceprovider to determine whether or not it should authorize the queryrequest. Based on a review of all the applicable policies, if the querycan be authorized and if the indexer cluster associated with the clustermaster has capacity to run the query, the cluster master will grant aslot back to the search head along with the addresses of the availableindexers in the indexer cluster. In one embodiment, the search head mayalready have the addresses of the available indexers based on a priorheartbeat response message that provided the list of active indexers andtheir addresses to the search head. If the query is not authorized or ifthe indexer cluster does not have capacity to run the query, no slot canbe granted at that time and, accordingly, no authorization token wouldbe transmitted to the search head.

Subsequently, the search head will use the information regarding theaddresses of the available indexers provided by the cluster master tocommunicate the query directly to the indexers in the indexer cluster1704. After the indexer cluster 1704 has responded with the queryresults, the search head can return the slot back to the cluster master1706.

Accordingly, the cluster master advantageously ensures that when queryloads are high, query throughput of the deployment does not crash downcompletely. Further, the cluster master also ensures that noconfigurations are exposed at the search head. For example, noconfiguration information pertaining to the number of permittedconcurrent queries on the indexer clusters, the type of queries, thetime range of queries, the resource usage metrics, etc. are exposed atthe search head and, therefore, cannot be tampered with intentionally orunintentionally by a client. This prevents rogue search headadministrators from misconfiguring the search heads to intentionallyharm cloud indexers in a hybrid environment.

Further, the cluster master provides several advantages for multi-tenantdeployments as well. For example, the cluster master may be able toprovide different levels of service for different organizations. Ahigh-bandwidth demanding organization may be able to receive more slotsfrom a cluster master than an organization that does not have highbandwidth requirements. In other words, the cluster master allowspreferential treatment to be provided to high-bandwidth requiringorganizations. Further, in multi-tenant environments, the cluster masteradvantageously allows higher preference to be given to securityapplications. In other words, if an organization is running queries fromsecurity applications, a cluster master can be configured to allocatemore slots to such queries.

Another significant advantage of cluster master in multi-tenantdeployments is the ability to maintain a centralized history of dataaccess. If multiple tenats are, for example, allowed to search a singlerepository of data on an indexer cluster, it is beneficial to keep trackof which tenants accessed the data at any given point in time. .Previously this information would only be logged on the individualsearch heads. If an organization chose to clean up all the logs on itssearch head or just not forward those logs to the indexing layer, thisinformation would be lost. Alternatively, the data on the search headsmay easily be tampered with . However, since the cluster master inmulti-tenant deployments can advantageously be under the control of thecluster master, the service provider can maintain reasonably trustworthyand accurate accounts of all the users/roles that accessed the data thatthe service provider was entrusted with.

2.14.1.1 Policy Settings and Enforcement Mode

In one embodiment, the service provider directly manages configurationson the cluster master. For example, configurations related to the typeof queries, maximum number of concurrent queries, time range of queriesetc. allowed on an associated indexer cluster may be directly programmedinto a cluster master by the service provider. Once the service providerhas programmed a cluster master with the configurations, theconfigurations are published to the search heads. However, as mentionedpreviously, no configurations are exposed at the search head level. Inother words, no client or client-side administrator would have controlto adjust the configurations for a cluster master at a search head.Therefore, a service provider would retain control over all the globalquota configurations for the cluster masters deployed in the cloud.

The cluster master can be statically or dynamically configured by theservice provider with various policy settings related to the globalquota requirements that are referenced to determine whether a particularquery will be dispatched to the cluster. Furthermore, policies can beset dynamically or statically. Exemplary static policies can includelimits on the number of concurrent queries that can be executed on anindexer cluster, the frequency with which queries can be executed, etc.The policies may also be set dynamically based on, for example,introspective measurements, e.g., I/O levels, CPU usage etc.

In one embodiment, the number of maximum concurrent queries may be astatically configurable policy on the cluster master. In other words, acluster master may permit a maximum number of concurrent queries on anassociated indexer cluster at any given time on a “first come, firstserve” basis. Accordingly, the cluster master may allocate slots (ortokens) to the search heads for running queries based on the number ofavailable slots. For example, cluster master 1614 a may permits 100concurrent queries. If less than 100 queries are concurrently running onindexer cluster 1610 a, then additional incoming queries from searchheads 1604 a-c and 1605 a-b would be permitted to receive slots ortokens from the cluster master 1614 a for executing the queries on afirst come, first serve basis. Subsequent to the execution of the query,the slot or token would be returned by the search head to the clustermaster.

In an embodiment, a cluster master may allocate a designated number ofslots to each search head from which it receives queries. For example,cluster master 1614 a may allocate 50 concurrent query slots for searchhead 1604 a and 100 concurrent query slots for search head 1604 b.Search head 1604 b may receive additional slots because it may berunning a security-related or high priority application. Or, forexample, a higher number of slots may be granted to a client with ahigher quota priority. The cluster master therefore ensures that usersor queries with a higher quota priority will not be affected as would beexpected of a multi-tenant deployment.

In one embodiment, the frequency with which queries are allowed to beexecuted may be another statically configurable policy on the clustermaster. As mentioned earlier, collection queries may be saved andscheduled to run periodically. A service provider may configure acluster master to not allow the results of a scheduled query to updatemore than a predetermined number of times during a certain time periodto prevent extra load from being put on an indexer cluster. This may beespecially relevant in cases where the scheduled query is acomputationally exhaustive one. Alternatively, a service provider mayonly want to designate a certain number of slots to run scheduledqueries so that slots can be available for users to run new queriesusing the user interface.

In one embodiment, another statically configurable policy may beprovenance. In other words, a service provider may be able to configurea cluster master to impose restrictions or limitations on incomingqueries based on the source or the type of queries allowed to beexecuted.

For example, a query can be an ad hoc query driven by a user inputting aquery from a command line interface, e.g., search screen 600 in FIG. 6Aincludes a search bar 602 that accepts user input in the form of asearch string. Alternatively, queries may be driven by an application,e.g., SPLUNK® ENTERPRISE or SPLUNK® APP FOR ENTERPRISE SECURITY. By wayof further example, a query may be a “real-time search,” which, asexplained earlier, can be open-ended, e.g., a query could request anyevents where the server response time is over 1 second in the last hourand further request that the query results continue to be updated.

A service provider may be able to configure the cluster master to imposelimitations on the various types of queries. For example, investigativequeries run from the command line interface may be given the highestpriority or the greatest number of slots so that a user would never haveto wait for a query to be executed. By comparison, a query driven by anapplication or a real-time query may be given a lower priority or afewer number of slots. Alternatively, an application query or real-timequery may serve a high priority security purpose and a user may,therefore, want to prioritize it over all other queries. Real-timequeries can be very computationally intensive, so a service provider mayneed to adjust the configurations to limit the number of concurrentreal-time queries.

As mentioned earlier, the policies may also be set dynamically based on,for example, introspective analysis. In an embodiment, the policies canbe set dynamically based on metrics reported in the heartbeat messagesbetween the indexer clusters and the cluster masters. For example, thecluster master could distribute I/O, CPU or memory credits to searchheads based on the metrics reported by indexer clusters regarding theI/O, memory or CPU usage for particular types of queries or queries froma particular search head. This would allow the policies of a clustermaster to be set dynamically based on introspective measurementsregarding the performance of certain types of queries or queries run bycertain types of search heads. When a particular search head has run outof I/O, CPU or memory credits, for example, no more queries may beaccepted from that search head. For example, a search head may be givenenough credits to run 1000 Gbps of I/O and any further requests by thesearch head may be canceled.

In one embodiment, a dynamically configurable policy may be related tothe indexes searched. The cluster master may be configured to limit theindexes that are searchable on a particular indexer cluster based on theinformation it has gathered about the indexers in the indexer cluster.This would, therefore, prevent a search head from exhaustively searchingthe entire indexer cluster.

As mentioned earlier, each indexer stores the events with an associatedtimestamp in a data store. The stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event.Organizing events into buckets optimizes time-based searching because itallows an indexer to search only the relevant buckets when responding toa query. Further, as mentioned previously, if a bucket comprises olddata and is designated as “cold,” it can be archived in slower memory.Because the cold buckets may be stored on a hard disk on a separatenode, accessing them may result in significant time delay.

In one embodiment, a dynamically configurable policy may limit thenumber of cold buckets that need to be decompressed in order to executea query. For example, a query may require decompressing and searchingold data that is only available on hard disk. The dynamic policy may beconfigured to prevent any search heads from running a query thatrequires decompressing more than a predetermined maximum number of coldbuckets containing the requested old data. Alternatively, the policy mayqueue or delay the execution of such a query.

In an embodiment, an incoming query is evaluated by the cluster masterusing the static and dynamic policies implemented by the serviceprovider. The response of the search head to an incoming query based onan evaluation of all applicable policies set by a service provider isreferred to as an “enforcement mode.” As mentioned previously, thecluster master periodically exchanges heartbeat messages with a searchhead. In one embodiment, a cluster master periodically publishes itsenforcement mode to a search head as part of a response heartbeatmessage. Information regarding the enforcement mode may be stored in abinary file at the search head once the cluster master advertises it tothe search head in a response heartbeat message. The search head can usethe published enforcement mode from a cluster master to determine how torespond in the event a cluster master does not grant it a slot or ifcontact cannot be established with the cluster master. In other words,the search head uses the enforcement mode published by the clustermaster to determine the manner in which to dispense with a query in theevent that a slot request is denied by a cluster master.

In an embodiment, the cluster master is responsible for enforcing theenforcement mode. In other words, the cluster master is responsible fordispensing with a query in the event that a slot cannot be granted. Forexample, if a slot cannot be granted, the cluster master would beresponsible for queuing or dropping the query instead of the search headin accordance with the enforcement mode.

In an embodiment, the cluster master can act as a fail-safe for thesearch head. If the search head, either as a result of some error (orbug) or malicious tampering of the binary file, attempts to dispatch aquery to the indexers in spite of being denied a slot by the clustermaster, the cluster master can prevent the search head from dispatchingthe query. In other words, the cluster master may act as the finalauthority for enforcing the enforcement mode. As noted above, thecluster master is capable of advertising the enforcement mode at thesearch head along with the list of available indexers including theiraddresses. In another embodiment where the cluster master may beprogrammed to act as a fail-safe, if the cluster master receives anindication that a search head is incapable of reliably enforcing theenforcement mode, it may also withhold the updated list of availableindexers including their addresses from the search head.

For example, an enforcement mode can prescribe an action to be taken inthe event a slot cannot be granted and, further, can prescribe an actionto be taken if no response is received from a cluster master. Forexample, an enforcement mode can be set to “strong cancel,” “weakcancel,” “strong queue,” and “weak queue.” The “cancel” and “queue”component of the enforcement mode is a prescription for an action to betaken when an explicit “No” is received from a cluster master. The“strong” or “weak” component of the enforcement mode prescribes anaction to be taken if no response is received at all from the master.For example, an enforcement mode can be set to either a “strong cancel”or a “weak cancel” mode. If a query is not allowed because it exceedsthe number of allowed concurrent queries, the cluster master will denythe slot request from the search head. In a strong cancel mode, thesearch head would not be allowed to dispatch the query at all. In otherwords, a strong cancel would simply cancel the query request from thesearch head if any of the policy settings are violated, thereby,providing a strong protection for the indexer clusters. Additionally, in“strong cancel” mode, the query is canceled if no response is receivedfrom the cluster master by the search head.

On the other hand, a weak cancel mode may cancel the query if anexplicit “No” is received from the cluster master, but if the clustermaster does not respond, the weak cancel mode would allow the searchhead to dispense the query to the active indexers in the cluster.Accordingly, a weak cancel mode may provide weaker protection for theindexer clusters.

By way of further elaboration, the enforcement mode of a cluster masteralso determines the response strategy of a search head that cannotestablish contact with the cluster master. For example, if the searchhead cannot establish contact with the cluster master to acquire a slotwhen attempting to execute a query , and the cluster master haspreviously published to the search head that it is running in strongcancel mode, the search head will simply cancel the query and take nofurther action. On the other hand, if the search head has informationfrom a heartbeat message that the cluster master is running in weakcancel mode, then it may send the query request directly to the indexercluster, thereby, circumventing the cluster master. While a serviceprovider may prefer to run the cluster master in strong cancel mode,occasionally, the service provider may be willing to sacrificeperformance for availability depending on a client's preference.

Similarly, in a “strong queue” mode, the query will be queued if eitheran explicit

“No” is received from the cluster master or if no response is receivedfrom the cluster master. By contrast, in a “weak queue” mode, the querywill be queued if an explicit “No” is received from the cluster master.However, if no response is received from the cluster master, the querymay be allowed to proceed to the active indexers in the cluster,thereby, circumventing the cluster master.

In an embodiment, variations to the various enforcement modes may alsobe programmable. For example, different queuing options in “strongqueue” or “weak queue” may be configured depending on the type of query.By way of example, a query may be queued only if it is an ad hoc queryinputted through a user interface, while other types of queries maysimply be dropped. Similarly, the query may be skipped or delayed if itis a scheduled query and has a lower priority level. In an embodiment, adynamic determination may be made regarding the number of pre-queueditems. If the number of queries in the queue is greater than athreshold, the query is canceled. Otherwise it is queued along with theother queries.

The conglomeration of the “enforcement mode” and “policy settings” isreferred to as a “configuration policy.” The configuration policy of acluster master will determine the rules of interaction between a clustermaster and associated search heads and indexer clusters. In other words,a cluster master will refer to an enabled configuration policy todetermine whether or not to grant slots to incoming queries from thesearch heads to access the associated indexer cluster. Further, a searchhead will use the enforcement mode published by the cluster master in aheartbeat response message to determine its behavior in the event a slotcannot be granted. However, as mentioned above, in certain embodiments,the cluster master may be responsible for enforcing the enforcement moderather than the search head.

FIG. 18 shows a block diagram illustrating the manner in which heartbeatmessages between a cluster master and a search head are used tocommunicate configuration policy information in accordance with thedisclosed embodiments. As explained previously, in an embodiment,cluster masters communicate with search heads using heartbeat messages,which are periodic messages that are sent from the search head to thecluster master requesting an updated list of indexers available forexecuting queries. In response, a cluster master provides a heartbeatresponse message back to the search head with an updated list of activeindexers including their addresses. In an embodiment, a cluster masteruses the response heartbeat message to provide additional information tothe search heads regarding the configuration policy being used by thecluster master.

As shown in FIG. 18, a search head 1808 can send a heartbeat message toa cluster master requesting a list of active indexers. Alternatively,the search head 1808 can send an out of band message to the clustermaster requesting a slot to run an incoming query. Any message from thesearch head requesting a slot typically contains information regardingthe query, e.g., the metadata associated with the query, the type ofquery, the application generating the query, the user running the query,etc. The cluster master typically uses the information received todetermine whether or not to grant an authorization token to the searchhead.

In an embodiment, the cluster master piggybacks on the existingheartbeat response messages sent from a cluster master to a search headto transmit information regarding its enforcement mode and/orauthorization tokens for slot grant requests.

FIG. 18 illustrates three exemplary configuration policies that may beavailable on the cluster master. A service provider may be able to setany of these configuration policies on the cluster master to determinethe policies and enforcement mode used by the cluster master in itscommunications with the search head. The information regarding theconfiguration policy (and the associated policy and enforcement modesettings) can be published to the search head using the heartbeatresponse message from the cluster master to the search head. In anembodiment, only the “enforcement mode” may need to be published oradvertised at the search head. Note, that while FIG. 18 illustrates onlythree exemplary modes, the cluster master may have any number ofpotential configuration policies available.

Configuration policy One 1802 comprises a “strong cancel” mode. Underthe “strong cancel” mode, for example, if any of the policies, e.g., Maxconcurrent searches, Frequency, Ad hoc searches, Real-Time searches orApplication searches are not adhered to, the search request by thesearch head is canceled. For example, if there are already 100concurrent searches that search head 1808 is running on the clustermaster, any additional query will be rejected by the search head. By wayof further example, any ad hoc search over 50 will also be rejected.Similarly, any real time query over 10 will be rejected and anyapplication search over 40 will be rejected. Further, if contact cannotbe made by the cluster master, the query will be dropped or canceled.

Configuration policy Two 1804 comprises a “weak cancel” mode. Under the“weak cancel” enforcement mode, if any of the policies, e.g.,“Max_concurrent_searches” for search head 1 or search head 2 isviolated, the query will be canceled if no slot request is granted. Forexample, if there are 80 concurrent queries already running on searchhead 1808, any additional incoming query will be canceled. However, ifthe search head is not able to make contact with the cluster master,then the query may be dispatched to the active indexers in the clusterregardless.

Configuration policy Three 1806 also comprises a “weak cancel” mode.However, as shown in FIG. 16, the policies that dictate theconfiguration policy are different from the policies that dictateconfiguration policy 1804.

In an embodiment, when the search head receives the configuration policy(including all the associated policies and enforcement mode information)as part of a heartbeat response from the cluster master, it incorporatesthe information into a binary file 1710 within the search head (as shownin FIG. 17). Accordingly, the cluster master publishes the configurationpolicy information to the search head and enforces the configurationpolicy by hard-coding the configuration policy information inside abinary within the search head. Because the information is hard-codedwithin a binary, a client-side user of the search head is not exposed toany configuration information. Accordingly, a client-side user has nocontrol over the configuration policy of the cluster master or theunderlying indexer cluster.

The search head uses the configuration policy information hard-codedwithin the binary 1710 to determine the manner in which to interact withthe cluster master in order to secure a slot for performing the query.For example, if a search head is not able to contact the cluster masterand is configured with a “strong cancel” enforcement mode, it willsimply cancel the query. Similarly, if a “weak cancel” enforcement modeis hard-coded within the binary file at the search head, the search headwould know to dispatch a query to the indexers even if no contact can beestablished with the cluster master.

In an embodiment, as part of a heartbeat message from a search head to acluster master, the search head has to report its version to the clustermaster. If a cluster master determines that the search head has not beenupdated and cannot be configured with the policy and enforcement modesettings, it may refuse to give the out-of-date search head access tothe underlying indexer cluster. Or it may not provide the out-of-datesearch head with a list of the active indexers.

In an embodiment, if one of the policies is violated or if the searchhead is not able to make connection with the cluster master, the searchhead will publish an alert to the client-side user, either at the searchhead or on a client-side terminal connected to the search head, with amessage explaining the failure. For example, if a search head is notable to get a slot because the maximum number of concurrent searches hasbeen exceeded, the user may receive an alert on the user interfacecontaining information regarding the policy that was in violation. In adifferent embodiment, the client is not exposed to the policy that isviolated and simply receives an error message.

In an embodiment, if a cluster master malfunctions or needs to beupgraded, the configuration policy of the cluster master can be changedto “null.” Because the cluster master uses a publishing model andpublishes its policies to the search head, it can communicate the “null”policy to the search head in a heartbeat response message. Accordingly,the cluster master can advantageously apprise all search headsassociated with it that effectively no configuration policy needs to befollowed. This avoids the problem of queries suspending in the eventthat the cluster master becomes a single point of failure. If thecluster master sets the configuration policy to “null”, the search headscan be configured to access the associated indexers or indexer clustersdirectly, thereby, circumventing the cluster master while it is beingupgraded or repaired.

FIG. 19 presents a flowchart illustrating an exemplary process in whicha search head secures authorization from a cluster master prior todispatching a query in accordance with the disclosed embodiments.

At step 1902, the search head transmits a slot request to the clustermaster. This request is typically an out of band request. In response toreceiving the request, the cluster master will evaluate the query andcheck against all the applicable policies set by a service provider todetermine whether or not it should authorize the query request.

Based on a review of all the applicable policies, the cluster masterwill respond and the search head will receive the response at step 1904.If the query can be authorized and if the indexer cluster associatedwith the cluster master has capacity to run the query , the clustermaster will grant a slot back to the search head along with theaddresses of the available indexers in the indexer cluster. If the queryis not authorized or if the indexer cluster does not have capacity torun the query, no slot can be granted at that time to the search head.

If the slot request is denied, then at step 1914, the search head willcheck the enforcement mode that is hard-coded to determine how it shouldrespond to the denial of the request. For example, if a “strong cancel”enforcement mode is hard-coded inside its binary, it will cancel therequest completely even where no contact can be established with thecluster master. On the other hand, if a “weak cancel” mode will resultin the query being dispatched if no contact can be established with themaster.

If the slot is granted by the cluster master, the search head willreceive an authorization token and a list of active indexers includingtheir addresses. The search head will then transmit the query to theactive indexers using the addresses received from the cluster master atstep 1908. In an embodiment, the cluster mater keeps track of the searchheads that have been provided authorization tokens. In other words, thecluster master maintains a reservation of the authorization token forthe search head while the query is being run on the active indexers.

At step 1910, the search head receives the query results back from theactive indexers.

Finally, at step 1912, the search head releases the slot back to thecluster master. The cluster master can then revoke the reservation ofthe authorization token.

2.15. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent query facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, California. HUNK® represents an analytics platform thatenables business and IT teams to rapidly explore, analyze, and visualizedata in Hadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received query request to identifyrequest parameters. If a query request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the query requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the queryrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received query request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indexes”). AnERP process provides an interface through which the search head 210 mayaccess virtual indexes.

Thus, a query reference to an index of the system relates to a locallystored and managed data collection. In contrast, a query reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indexes, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indexes mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by the dataintake and query system 108. Alternatively or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indexes referenced in a query request, or the searchhead may spawn different ERP processes for different virtual indexes.Generally, virtual indexes that share common data configurations orprotocols may share ERP processes. For example, all query references toa Hadoop file system may be processed by the same ERP process, if theERP process is suitably configured. Likewise, all query references to anSQL database may be processed by the same ERP process. In addition, thesearch head may provide a common ERP process for common external datasource types (e.g., a common vendor may utilize a common ERP process,even if the vendor includes different data storage system types, such asHadoop and SQL). Common indexing schemes also may be handled by commonERP processes, such as flat text files or Weblog files.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in aquery request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indexes that are referred to in a query request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a query request from the searchhead 210. The search head may optimize the received query request forexecution at the respective external virtual index. Alternatively, theERP process may receive a query request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in / standard out,etc.). In this way, the ERP process receives the query request from aclient device such that the query request may be efficiently executed atthe corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate queryrequests in the protocol and syntax of the respective virtual indexes1114, 1116, each of which corresponds to the query request received bythe search head 210. Upon receiving query results from theircorresponding virtual indexes, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.15.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns query results in real time, with minimalprocessing, in response to the query request. The reporting modeprovides results of a query request with processing of the query resultsprior to providing them to the requesting search head, which in turnprovides results to the requesting client device. ERP operation withsuch multiple modes provides greater performance flexibility with regardto report time, query latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a query request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or query requester interim results that are responsive to thequery. Meanwhile, in this mixed mode, the ERP also operates concurrentlyin reporting mode, processing portions of raw data in a mannerresponsive to the query. Upon determining that it has results from thereporting mode available to return to the search head, the ERP may haltprocessing in the mixed mode at that time (or some later time) bystopping the return of data in streaming mode to the search head andswitching to reporting mode only. The ERP at this point starts sendinginterim results in reporting mode to the search head, which in turn maythen present this processed data responsive to the query request to theclient or query requester. Typically the search head switches from usingresults from the ERP's streaming mode of operation to results from theERP's reporting mode of operation when the higher bandwidth results fromthe reporting mode outstrip the amount of data processed by the searchhead in the ]streaming mode of ERP operation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe query request, and calculating statistics on the results. The usercan request particular types of data, such as if the query itselfinvolves types of events, or the query request may ask for statistics ondata, such as on events that meet the query request. In either case, thesearch head understands the query language used in the received queryrequest, which may be a proprietary language. One examplary querylanguage is Splunk Processing Language (SPL) developed by the assigneeof the application, Splunk Inc. The search head typically understandshow to use that language to obtain data from the indexers, which storedata in a format used by the SPLUNK® Enterprise system.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thequery support system's native format (e.g., SPL if SPLUNK® ENTERPRISE isused as the query support system) to a query request format that will beaccepted by the corresponding external data system. The external datasystem typically stores data in a different format from that of thequery support system's native index format, and it utilizes a differentquery language (e.g., SQL or MapReduce, rather than SPL or the like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the query support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the query). An advantage of mixed mode operation isthat, in addition to streaming mode, the ERP process is also executingconcurrently in reporting mode. Thus, the ERP process (rather than thesearch head) is processing query results (e.g., performing eventbreaking, timestamping, filtering, possibly calculating statistics ifrequired to be responsive to the query request, etc.). It should beapparent to those skilled in the art that additional time is needed forthe ERP process to perform the processing in such a configuration.Therefore, the streaming mode will allow the search head to startreturning interim results to the user at the client device before theERP process can complete sufficient processing to start returning anyquery results. The switchover between streaming and reporting modehappens when the ERP process determines that the switchover isappropriate, such as when the ERP process determines it can beginreturning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the query request. That is, the ERPprocess doesn't have to take time to first return raw data to the searchhead. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the query request. Alternatively,the ERP process can be configured to operate in the reporting mode only.Also, the ERP process can be configured to operate in streaming mode andreporting mode concurrently, as described, with the ERP process stoppingthe transmission of streaming results to the search head when theconcurrently running reporting mode has caught up and started providingresults. The reporting mode does not require the processing of all rawdata that is responsive to the query request before the ERP processstarts returning results; rather, the reporting mode usually performsprocessing of chunks of events and returns the processing results to thesearch head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a query result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thequery request and handling all the computation that a native indexerprocess would otherwise perform. In this way, the configured ERP processprovides greater flexibility in features while operating according todesired preferences, such as response latency and resource requirements.

2.16. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a query thatderives a KPI value from the machine data of events associated with theentities that provide the service. Information in the entity definitionsmay be used to identify the appropriate events at the time a KPI isdefined or whenever a KPI value is being determined. The KPI valuesderived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to query processing. Aggregate KPIsmay be defined to provide a measure of service performance calculatedfrom a set of service aspect KPI values; this aggregate may even betaken across defined timeframes or across multiple services. Aparticular service may have an aggregate KPI derived from substantiallyall of the aspect KPI's of the service to indicate an overall healthscore for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’warning,“normal,' and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation query” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation query may additionally or alternatively causea service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation query, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationquery that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

What is claimed is:
 1. A method for performing a query, the methodcomprising: transmitting a list of active indexers in an indexer clusterfrom a cluster master for receipt by a first search head, wherein thecluster master is communicatively coupled with an indexer clustercomprising a plurality of indexers; receiving a first slot request atthe cluster master from the first search head in response to a query,wherein the first search head is operable to transmit the query to theactive indexers for execution if granted the slot request; determiningif the first slot request can be granted; and responsive to adetermination that the first slot request can be granted, transmittingan indication of a grant for a slot to the first search head.
 2. Themethod of claim 1, wherein the list of the active indexers istransmitted as part of a heartbeat response message.
 3. The method ofclaim 1, wherein the first search head is one of a plurality of searchheads operable to be communicatively coupled to the cluster master. 4.The method of claim 1, wherein the indication of the grant for the slotcomprises an authorization token.
 5. The method of claim 1, furthercomprising: responsive to a determination that the first slot requestcannot be granted, transmitting a denial of the slot request to thefirst search head and dispensing with the query in accordance with anenforcement mode, wherein the enforcement mode comprises taking anaction selected from the group consisting of: canceling the query,transmitting the query to the active indexers, queuing the query,delaying the query and canceling the query if a number of entries in asearch queue are above a threshold value.
 6. The method of claim 1,wherein the indication of the grant for the slot comprises a firstauthorization token, and wherein the method further comprises: receivinga release of the slot from the first search head; and responsive to adetermination that a second slot request can be granted to a secondsearch head from the plurality of search heads, transmitting a secondauthorization token for the slot to the second search head.
 7. Themethod of claim 1, wherein the indication of the grant for the slotcomprises an authorization token, and wherein the method furthercomprises: while the authorization token is granted for the first searchhead, maintaining a reservation of the authorization token for the firstsearch head; receiving a release of the slot from the first search head;and releasing the reservation of the authorization token.
 8. The methodof claim 1, wherein the transmitting is in response to a request for thelist of active indexers in a heartbeat message from the first searchhead to the cluster master.
 9. The method of claim 1, wherein the firstsearch head is one of a plurality of search heads operable to becommunicatively coupled to the cluster master, wherein the clustermaster, the first search head and the indexer cluster are located in acloud network, wherein a second search head of the plurality of searchheads is located at an on-premises location of an organization, andwherein the first search head and the second search head are under thecontrol of the organization.
 10. The method of claim 1, wherein thefirst search head is one of a plurality of search heads operable to becommunicatively coupled to the cluster master, and wherein the clustermaster, the plurality of search heads and the indexer cluster arelocated in a cloud network.
 11. The method of claim 1, wherein the firstsearch head is one of a plurality of search heads operable to becommunicatively coupled to the cluster master, wherein the first searchhead is located at an on-premises location of an organization, whereinthe cluster master, the indexer cluster and the remaining search headsin the plurality of search heads are located in a cloud network.
 12. Themethod of claim 1, wherein the determining if the first slot request canbe granted comprises evaluating a plurality of policies, wherein theevaluating the plurality of policies comprises consulting a plurality ofstatic policies selected from the group consisting of: maximum number ofconcurrent searches, frequency of searches, source of searches, and typeof search.
 13. The method of claim 1, wherein the determining if thefirst slot request can be granted comprises evaluating a plurality ofpolicies, wherein the evaluating the plurality of policies comprisesconsulting a plurality of dynamic policies selected from the groupconsisting of: I/O level usage of the search head, CPU usage by thesearch head, memory usage by the search head, types of indexes accessedby the query, and number of archived indexes accessed by the query. 14.A non-transitory computer-readable medium having computer-readableprogram code embodied therein for causing a computer system to perform amethod for performing a query, the method comprising: transmitting alist of active indexers in an indexer cluster from a cluster master forreceipt by a first search head, wherein the cluster master iscommunicatively coupled with an indexer cluster comprising a pluralityof indexers; receiving a first slot request at the cluster master inresponse to a query from the first search head, wherein the first searchhead is operable to transmit the query to the active indexers forexecution if granted the slot request; determining if the first slotrequest can be granted; and responsive to a determination that the firstslot request can be granted, transmitting an indication of a grant for aslot to the first search head.
 15. The computer-readable medium of claim14, wherein the list of the active indexers is transmitted as part of aheartbeat response message.
 16. The computer-readable medium of claim14, wherein the first search head is one of a plurality of search headsoperable to be communicatively coupled to the cluster master.
 17. Thecomputer-readable medium of claim 14, wherein the indication of thegrant for the slot comprises an authorization token.
 18. Thecomputer-readable medium of claim 14, wherein the method furthercomprises: responsive to a determination that the first slot requestcannot be granted, transmitting a denial of the slot request to thefirst search head and dispensing with the query in accordance with anenforcement mode, wherein the enforcement mode comprises taking anaction selected from the group consisting of: canceling the query,transmitting the query to the active indexers, queuing the query,delaying the query and canceling the query if a number of entries in asearch queue are above a threshold value.
 19. A system comprising: aprocessing device communicatively coupled with a memory and configuredto: transmit a list of active indexers in an indexer cluster from acluster master for receipt by a first search head, wherein the clustermaster is communicatively coupled with an indexer cluster comprising aplurality of indexers; receive a first slot request at the clustermaster in response to a query from the first search head, wherein thefirst search head is operable to transmit the query to the activeindexers for execution if granted the slot request; determine if thefirst slot request can be granted; and responsive to a determinationthat the first slot request can be granted, transmit an indication of agrant for a slot to the first search head.
 20. The system of claim 19,wherein the list of the active indexers is transmitted as part of aheartbeat response message.
 21. The system of claim 19, wherein thefirst search head is one of a plurality of search heads operable to becommunicatively coupled to the cluster master.
 22. The system of claim19, wherein the indication of the grant for the slot comprises anauthorization token.